Browse Models
Launch a dedicated cloud GPU server running Laboratory OS to download and run Bark using any compatible app or framework.
Must be used with a compatible app, notebook, or codebase. May run slowly, or not work at all, depending on local system resources, particularly GPU(s) and available VRAM.
Experiment with various cutting-edge audio generation models, such as Bark (Text-to-Speech), RVC (Voice Cloning), and MusicGen (Text-to-Music).
Bark is a transformer-based text-to-audio model that generates multilingual speech, music, and sound effects by converting text directly to audio tokens using EnCodec quantization. The model supports over 13 languages with 100+ speaker presets and can produce nonverbal sounds like laughter through special tokens, operating via a three-stage pipeline from semantic to fine audio tokens.
Bark is a transformer-based generative text-to-audio model developed by Suno. Designed to produce high-fidelity, multilingual speech as well as a wide range of non-speech audio including music, background noises, and sound effects, Bark distinguishes itself by directly converting text prompts to audio—bypassing traditional intermediate representations like phonemes. Bark aims to address a broad set of use cases in content generation, accessibility, and audio synthesis, supporting functionality that spans expressive speech, singing, and varied acoustic phenomena. The model is publicly released under the MIT License, with both code and model weights available for research and development.
A schematic illustrating Bark's text-to-audio pipeline, where a structured text input (including speaker labels and nonverbal cues) is transformed into an audio waveform.
Bark is notable for its fully generative approach to audio synthesis. Unlike conventional text-to-speech systems that rely on deterministic or narrowly constrained outputs, Bark generalizes to a diverse array of text instructions. It supports both speech with natural prosody and a spectrum of non-speech sounds, such as music, background noise, laughter, sighs, and other nonverbal vocalizations. Users can include special tokens, such as [laughs], [sighs], [music], and speaker cues (e.g., [MAN], [WOMAN]) to influence the acoustic characteristics of the output. The model processes both language and non-linguistic instructions, enabling complex and expressive audio generations.
Multilingual support is an integral aspect of Bark; the model can infer language automatically from the input text and generate speech with native accents. Among supported languages are English, German, Spanish, French, Hindi, Italian, Japanese, Korean, Polish, Portuguese, Russian, Turkish, and Simplified Chinese, as documented on the official repository. While English generation achieves the highest quality in the current iteration, improvements for other languages are planned via continuing research and scaling.
Bark also includes over 100 speaker presets, enabling the synthesis of diverse voices across various languages. These presets control features such as tone, pitch, emotion, and prosody for more contextualized and expressive outputs. Although Bark cannot clone custom voices, it can generate novel and varied speaker characteristics with randomization.
Bark's architecture follows a multi-stage transformer pipeline inspired by autoregressive large language models. The design leverages recent advances in audio representation, most notably through the use of the EnCodec quantized audio codec, which maps waveforms to discrete tokens. The overall workflow closely resembles prior work in models such as AudioLM and Vall-E.
The Bark model pipeline consists of three main stages:
In the first stage, input text is tokenized—using the BERT tokenizer—and mapped to semantic tokens that encapsulate the intended acoustic content, including both verbal and nonverbal elements.
Subsequently, semantic tokens are transformed into "coarse" audio tokens, which correspond to the primary codebooks of the EnCodec representation.
In the final stage, the model refines these coarse tokens to "fine" EnCodec tokens, thus enabling a high-fidelity reconstruction of complex audio waveforms.
Both small (80M parameters) and large (300M parameters) variants are available, with differences primarily in generation quality and resource requirements. Architectural details, parameter counts, and vocabulary sizes align with those detailed in the official documentation.
While the specific datasets and full training details have not been comprehensively disclosed, Bark’s training regime follows the transformer-based methodology popularized in recent text and audio generative research. The model is trained in an autoregressive manner, mapping text to semantic representations and then to audio tokens, as outlined in NanoGPT and adapted for audio by AudioLM-Pytorch. The adoption of EnCodec as the quantization backbone for audio tokens facilitates efficient representation and scalable training, a technique similarly employed in other generative audio models.
Bark's ability to generate expressive, multilingual, and nonverbal audio makes it suitable for a wide range of use cases. Notable applications include the development of accessibility tools via lifelike text-to-speech in multiple languages, automated narration for video content, synthetic voiceover for creative productions, the generation of music and sound effects, and linguistic research.
In practice, Bark can be run locally using the Hugging Face Transformers library or through the original Bark library. The model supports Python-based inference pipelines and provides options for fine-grained control over generation parameters and prompt structure. By default, Bark is designed for generating audio segments of approximately 13–14 seconds; however, long-form audio generation is supported via chunked inference strategies, as demonstrated in the official notebooks.
Bark was initially released in April 2023, and subsequently relicensed under the MIT License in May 2023, as recorded on the Hugging Face model card. Both a small and a large checkpoint are provided, optimized for different computational resources and performance requirements. The small variant is accessible at bark-small, while the primary model is available at bark.
Despite its versatility, Bark presents certain limitations. As a generative model, its outputs are inherently stochastic and may sometimes diverge from prompt intent. Output duration is optimized for segments under 14 seconds, and while Bark offers speaker and accent variation, it does not currently support individualized voice cloning. The model excels in English, with varying performance across other languages. To help mitigate unintended uses, Suno provides a classifier for identifying Bark-generated audio, as described in the official documentation.